scholarly journals Dynamic wake modeling and state estimation for improved model-based receding horizon control of wind farms

2017 ◽  
Author(s):  
Carl R. Shapiro ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme
Keyword(s):  
State Estimation ◽  
Wind Farms ◽  
Receding Horizon Control ◽  
Receding Horizon ◽  
Model Based ◽  
Improved Model ◽  
Wake Modeling

scholarly journals Model-based receding horizon control of wind farms for secondary frequency regulation

Wind Energy ◽  
2017 ◽  
Vol 20 (7) ◽  
pp. 1261-1275 ◽  
Author(s):  
Carl R. Shapiro ◽  
Pieter Bauweraerts ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme
Keyword(s):  
Wind Farms ◽  
Receding Horizon Control ◽  
Frequency Regulation ◽  
Receding Horizon ◽  
Model Based

scholarly journals Wind farms providing secondary frequency regulation: Evaluating the performance of model-based receding horizon control

2017 ◽  
Author(s):  
Carl R. Shapiro ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme
Keyword(s):  
Dynamic Model ◽  
Wind Farm ◽  
Wind Farms ◽  
Static Model ◽  
Receding Horizon Control ◽  
Frequency Regulation ◽  
Control Approach ◽  
Model Based ◽  
Model Control ◽  
Wake Model

Abstract. We investigate the use of wind farms to provide secondary frequency regulation for a power grid using a model-based receding horizon control framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and wake interactions, both of which play an important role in wind farm power production. In order to test the control strategy, it is implemented in a large eddy simulation (LES) model of an 84-turbine wind farm using the actuator disk turbine representation. Rotor-averaged velocity measurements at each turbine are used to provide feedback for error correction. The importance of including the dynamics of wake advection in the underlying wake model is tested by comparing the performance of this dynamic-model control approach to a comparable static-model control approach that relies on a modified Jensen model. We compare the performance of both control approaches using two types of regulation signals, "RegA'" and "RegD", which are used by PJM, an independent system operator in the Eastern United States. The poor performance of the static-model control relative to the dynamic-model control demonstrates that modeling the dynamics of wake advection is key to providing the proposed type of model-based coordinated control of large wind farms. We further explore the performance of the dynamic-model control via composite performance scores used by PJM to qualify plants for regulation. Our results demonstrate that the dynamic-model controlled wind farm consistently performs well, passing the qualification threshold for all fast-acting RegD signals. For the RegA signal, which changes over slower time scales, the dynamic-model control leads to average performance that surpasses the qualification threshold, but further work is needed to enable this controlled wind farm to achieve qualifying performance for all regulation signals.

scholarly journals Wind farms providing secondary frequency regulation: evaluating the performance of model-based receding horizon control

2018 ◽  
Vol 3 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Carl R. Shapiro ◽  
Johan Meyers ◽  
Charles Meneveau ◽  
Dennice F. Gayme
Keyword(s):  
Dynamic Model ◽  
Wind Farm ◽  
Wind Farms ◽  
Static Model ◽  
Receding Horizon Control ◽  
Frequency Regulation ◽  
Control Approach ◽  
Model Based ◽  
Model Control ◽  
Wake Model

Abstract. This paper is an extended version of our paper presented at the 2016 TORQUE conference (Shapiro et al., 2016). We investigate the use of wind farms to provide secondary frequency regulation for a power grid using a model-based receding horizon control framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and wake interactions, both of which play an important role in wind farm power production. In order to test the control strategy, it is implemented in a large-eddy simulation (LES) model of an 84-turbine wind farm using the actuator disk turbine representation. Rotor-averaged velocity measurements at each turbine are used to provide feedback for error correction. The importance of including the dynamics of wake advection in the underlying wake model is tested by comparing the performance of this dynamic-model control approach to a comparable static-model control approach that relies on a modified Jensen model. We compare the performance of both control approaches using two types of regulation signals, “RegA” and “RegD”, which are used by PJM, an independent system operator in the eastern United States. The poor performance of the static-model control relative to the dynamic-model control demonstrates that modeling the dynamics of wake advection is key to providing the proposed type of model-based coordinated control of large wind farms. We further explore the performance of the dynamic-model control via composite performance scores used by PJM to qualify plants for regulation services or markets. Our results demonstrate that the dynamic-model-controlled wind farm consistently performs well, passing the qualification threshold for all fast-acting RegD signals. For the RegA signal, which changes over slower timescales, the dynamic-model control leads to average performance that surpasses the qualification threshold, but further work is needed to enable this controlled wind farm to achieve qualifying performance for all regulation signals.

Newton-Type Receding Horizon Control and State Estimation - A Case Study

1996 ◽  
Vol 29 (1) ◽  
pp. 5917-5922 ◽  
Author(s):  
A. M'hamdi ◽  
A. Helbig ◽  
O. Abel ◽  
W. Marquardt
Keyword(s):  
State Estimation ◽  
Receding Horizon Control ◽  
Receding Horizon
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